1. Technical Field
The present invention relates to data mining and, in particular, to methods and systems for extracting meaningful information from temporal event data.
2. Description of the Related Art
Temporal event mining is the process of identifying and extracting latent temporal patterns from large complex event data. The goal of event mining is to derive insight that leads to useful information and true knowledge for an accurate understanding of the underlying event processes and relationships. It is the transformation from data to information to true knowledge that is of interest to business, the science, and government.
In this regard, finding optimal knowledge representations is important when one talks about abstract models as surrogates of the real world that must be understood by humans. Knowledge representation (KR) and reasoning is an area of artificial intelligence whose fundamental goal is to represent knowledge in a manner that facilitates the inference process from knowledge. An optimal knowledge representation should exhibit the following characteristics: i) minimalism, ii) interpretability, and iii) novelty. Further, the knowledge representation should be commensurate with human constraints and abilities so people can quickly absorb, understand, and make most efficient use of complex event data. All attempts to produce such a knowledge representation to date have fallen short. One drawback of these attempts is that symbolic languages specify temporal knowledge a priori, which limits the flexibility of learning unknown patterns in temporal event data.